Particle analyzers, such as hematology analyzers or flow cytometers, process biological samples for particle analysis. They measure physical properties of biological particles in biological samples for analysis. Exemplary physical property measurements are electro-optical measurements. The measured physical properties can be viewed as a multidimensional space. Each dimension in the multidimensional space corresponds to a measured physical property. Particles sharing similar physical properties group into clusters in the multidimensional space. Each cluster corresponds to a specific particle population. Due to the statistical distribution of the particles and the multiple dimensions involved, the process of identifying such clusters by an automated method or algorithm is generally a complex task.
One way to reduce such complexity is to use two-dimensional (2D) projections of the multi-dimensional space to perform classification or differentiation of the particles. For example, FIGS. 1A and 1B illustrate two conventional 2D projections of hematology data for white blood cell subpopulations contained in a normal whole blood sample. For example, the 2D projections can be 2D histograms obtained from multidimensional particle analysis data. A 2D histogram contains a set of two-dimensional bins. Each bin accumulates particle events appearing at the location of the bin. The accumulated value represents the projected particle density or count at the location. For instance, this count can be a count of the number of particles having data values that correspond to the bin location.
In FIG. 1A, image 110 is an orthogonal projection of the hematology data on RLS (rotated light scatter)-DC (direct current) dimensions. In image 110, pixel groups (or clusters) 112, 114, and 116 correspond to leukocyte populations, and in particular to monocytes, neutrophils, and eosinophils respectively. Cluster 118 corresponds to lymphocyte and basophil populations. In FIG. 1B, image 120 is an orthogonal projection of the same hematology data on OP (opacity)-DC dimensions. Opacity is a parameter obtained as a function of DC and RF (radio frequency). In image 120, cluster 122 corresponds to a monocyte population, cluster 124 corresponds to neutrophil, eosinophil and basophil populations, and cluster 128 corresponds to a lymphocytes population.
Conventional algorithms apply a segmentation method to separate the clusters in the 2D projections based on analysis of multiple one-dimensional (1D) histograms. A 1D histogram contains a set of one-dimensional bins accumulating particle events at the locations of the bins along that dimension. The accumulated values represent the particle density or count in that dimension. For example, in one conventional technique, an amplitude analysis is performed on multiple 1D histograms. The analyzing results are combined to produce a 2D segmentation. As shown in FIGS. 1A and 1B, the behavior and relationship among the white blood cell subpopulations is well defined for normal samples.
However, changes in the morphology, internal structure, and maturation process of the biological particles can alter the location, size, and shape of the clusters in a 2D histogram. These changes introduce additional complexity for conventional segmentation processes. This is particularly true when shifting and overlapping among the particle populations occur. For example, FIGS. 2A, 2B, 3A, and 3B illustrate orthogonal projections of data from two abnormal blood samples with heavily overlapped monocyte and neutrophil cell populations. In these figures, images 210 and 310 are orthogonal projections on RLS-DC dimensions and images 220 and 320 are orthogonal projections on OP-DC dimensions. In image 210, cluster 212 corresponds to monocyte and neutrophil populations. Cluster 216 corresponds to an eosinophil population. Cluster 218 corresponds to lymphocyte and basophil populations. In image 220, cluster 222 corresponds to monocyte, neutrophil, eosinophil, and basophil populations and cluster 228 corresponds to a lymphocyte population. In image 310, cluster 312 corresponds to monocyte and neutrophil populations. Cluster 316 corresponds to an eosinophil population. Cluster 318 corresponds to lymphocyte and basophil populations. In image 320, cluster 322 corresponds to monocyte, neutrophil, eosinophil, and basophil populations and cluster 328 corresponds to a lymphocyte population. In this example, the clusters commonly associated with the monocyte and neutrophil populations fall into a transition region Therefore, these populations cannot be consistently identified.
There are two general reasons the events can fall into transition regions, such as region 212 in FIG. 2A, region 222 in FIG. 2B, region 312 in FIG. 3A, and region 322 in FIG. 3B. First, as shown in FIGS. 2A and 2B, the events can correspond to heavily overlapped neutrophil and monocyte populations at regions 212 and 222. Second, as shown in FIGS. 3A and 3B, a single monocyte or neutrophil cluster can shift from its expected locations to regions 312 and 322.
While conventional segmentation approaches work well for normal samples and for some abnormal samples, they perform inconsistently with samples such as those yielding results similar to FIGS. 2A, 2B, 3A and 3B. The main reason for this inconsistent performance is conventional algorithms only rely on a search for peaks and valleys in multiple 1D histograms obtained from the rows and columns of the 2D histogram. As a result, conventional algorithms run into problems, for example, where peaks and valleys have merged together because of populations shifts.